Tiny Kinetics-400 for test

Overview

Kinetics-400迷你数据集

English | 简体中文

该数据集旨在解决的问题:参照Kinetics-400数据格式,训练基于自己数据的视频理解模型。

数据集介绍

Kinetics-400是视频领域benchmark常用数据集,详细介绍可以参考其官方网站Kinetics。整个数据集包含400个类别,全部文件大概需要135G左右的存储空间,下载起来比较困难。

Tiny-Kinetics-400同样包含400个类别,每个类别下仅有两条视频数据,分为train与val,可用于调试一些视频理解模型。

具体对比如下:

数据集 训练条数 验证条数 大小
Kinetics-400 234619 19761 135G
Tiny-Kinetics-400 400 400 420M

Tiny-Kinetics-400下载

目前提供了百度网盘的下载方式:

下载方式 链接
百度云 BaiduCloud (1cns)

抽帧Extract Frames

通常在训练视频理解模型时,会提前对视频文件进行抽帧,以此来加速训练过程。这里提供了抽帧脚本,且满足以下条件:

  • 每个视频只抽取300帧
  • 如果整个视频多于300帧,直接舍弃之后的视频帧
  • 如果整个视频少于300帧,复制最后的视频帧以填充至300帧

使用方式:

python ./tools/extract_frames.py --source_dir ~/data/tiny-kinetics-400/train_256 ~/data/kinetics400_30fps_frames/train
python ./tools/extract_frames.py --source_dir ~/data/tiny-kinetics-400/val_256 ~/data/kinetics400_30fps_frames/val

将meta文件移到视频帧目录下:

mv ./annotations/tiny_train.csv ~/data/kinetics400_30fps_frames/
mv ./annotations/tiny_val.csv ~/data/kinetics400_30fps_frames/

最终的目录结构如下:

kinetics400_30fps_frames/
├── train/
│   ├── abseiling/
│   │   ├──_4YTwq0-73Y_000044_000054
│   │   │  ├──frame_00001.jpg
│   │   │  ├──...
│   │   ├──...
│   ├──...
├── val/
│   ├── abseiling/
│   │   ├──-3B32lodo2M_000059_000069
│   │   │  ├──frame_00001.jpg
│   │   │  ├──...
│   │   ├──...
│   ├──...
├── tiny_train.csv
├── tiny_val.csv

TODO

  • 更多下载方式

参考

Owner
Data&Model&Loss
Multiple style transfer via variational autoencoder

ST-VAE Multiple style transfer via variational autoencoder By Zhi-Song Liu, Vicky Kalogeiton and Marie-Paule Cani This repo only provides simple testi

13 Oct 29, 2022
Nested Graph Neural Network (NGNN) is a general framework to improve a base GNN's expressive power and performance

Nested Graph Neural Networks About Nested Graph Neural Network (NGNN) is a general framework to improve a base GNN's expressive power and performance.

Muhan Zhang 38 Jan 05, 2023
This GitHub repo consists of Code and Some results of project- Diabetes Treatment using Gold nanoparticles. These Consist of ML Models used for prediction Diabetes and further the basic theory and working of Gold nanoparticles.

GoldNanoparticles This GitHub repo consists of Code and Some results of project- Diabetes Treatment using Gold nanoparticles. These Consist of ML Mode

1 Jan 30, 2022
PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Sharpness-aware Quantization for Deep Neural Networks This is the official repository for our paper: Sharpness-aware Quantization for Deep Neural Netw

Zhuang AI Group 30 Dec 19, 2022
A Real-World Benchmark for Reinforcement Learning based Recommender System

RL4RS: A Real-World Benchmark for Reinforcement Learning based Recommender System RL4RS is a real-world deep reinforcement learning recommender system

121 Dec 01, 2022
SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model

SEOVER-Master This code is the implementation of paper: SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model

4 Feb 24, 2022
Implementation for the paper SMPLicit: Topology-aware Generative Model for Clothed People (CVPR 2021)

SMPLicit: Topology-aware Generative Model for Clothed People [Project] [arXiv] License Software Copyright License for non-commercial scientific resear

Enric Corona 225 Dec 13, 2022
Content shared at DS-OX Meetup

Streamlit-Projects Streamlit projects available in this repo: An introduction to Streamlit presented at DS-OX (Feb 26, 2020) meetup Streamlit 101 - Ja

Arvindra 69 Dec 23, 2022
Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation (ICCV2021)

Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation (ICCV2021) This is the implementation of PSD (ICCV 2021),

12 Dec 12, 2022
Python package to generate image embeddings with CLIP without PyTorch/TensorFlow

imgbeddings A Python package to generate embedding vectors from images, using OpenAI's robust CLIP model via Hugging Face transformers. These image em

Max Woolf 81 Jan 04, 2023
PyTorch implementation of ARM-Net: Adaptive Relation Modeling Network for Structured Data.

A ready-to-use framework of latest models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, and etc.

48 Nov 30, 2022
Exploring Classification Equilibrium in Long-Tailed Object Detection, ICCV2021

Exploring Classification Equilibrium in Long-Tailed Object Detection (LOCE, ICCV 2021) Paper Introduction The conventional detectors tend to make imba

52 Nov 21, 2022
Code for CVPR2019 paper《Unequal Training for Deep Face Recognition with Long Tailed Noisy Data》

Unequal-Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data. This is the code of CVPR 2019 paper《Unequal Training for Deep Face Recognition

Zhong Yaoyao 68 Jan 07, 2023
CarND-LaneLines-P1 - Lane Finding Project for Self-Driving Car ND

Finding Lane Lines on the Road Overview When we drive, we use our eyes to decide where to go. The lines on the road that show us where the lanes are a

Udacity 769 Dec 27, 2022
Confident Semantic Ranking Loss for Part Parsing

Confident Semantic Ranking Loss for Part Parsing

Jiachen Xu 5 Oct 22, 2022
Public Code for NIPS submission SimiGrad: Fine-Grained Adaptive Batching for Large ScaleTraining using Gradient Similarity Measurement

Public code for NIPS submission "SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement" This repo co

Heyang Qin 0 Oct 13, 2021
Deep ViT Features as Dense Visual Descriptors

dino-vit-features [paper] [project page] Official implementation of the paper "Deep ViT Features as Dense Visual Descriptors". We demonstrate the effe

Shir Amir 113 Dec 24, 2022
ByteTrack: Multi-Object Tracking by Associating Every Detection Box

ByteTrack ByteTrack is a simple, fast and strong multi-object tracker. ByteTrack: Multi-Object Tracking by Associating Every Detection Box Yifu Zhang,

Yifu Zhang 2.9k Jan 04, 2023
Deep Image Matting implementation in PyTorch

Deep Image Matting Deep Image Matting paper implementation in PyTorch. Differences "fc6" is dropped. Indices pooling. "fc6" is clumpy, over 100 millio

Yang Liu 724 Dec 27, 2022
(CVPR 2021) Lifting 2D StyleGAN for 3D-Aware Face Generation

Lifting 2D StyleGAN for 3D-Aware Face Generation Official implementation of paper "Lifting 2D StyleGAN for 3D-Aware Face Generation". Requirements You

Yichun Shi 66 Nov 29, 2022